Transition gauge for electric aircraft

ABSTRACT

In an aspect, a propulsor gauge is provide that provides guidance during a transition of a mode of operation of an electric aircraft. Propulsor gauge may include a processor configured to identify a current thrust envelope parameter of one or more propulsors of electric aircraft. Processor may also determine a recommended range as a function of the propulsor parameter, where the recommended range includes a lower threshold and an upper threshold. The recommended range and thrust envelope parameter may then be shown on a display of propulsor gauge, where a measurement of current thrust envelope parameter relative to recommended range may be represented by an indicator.

FIELD OF THE INVENTION

The present invention generally relates to the field of electricaircraft. In particular, the present invention is directed to atransition gauge for an electric aircraft.

BACKGROUND

Modern aircraft, such as vertical landing and takeoff aircraft (VTOL)may include a set of rotors. Electric aircraft may transition fromvertical flight to edgewise flight.

SUMMARY OF THE DISCLOSURE

In an aspect, a transition gauge is provided. The propulsor gaugeincludes: a processor; a memory communicatively connected to theprocessor and configured to contain instructions configuring processorto: identify a thrust envelope parameter of the propulsor; determine arecommended range as a function of the propulsor parameter, wherein therecommended range comprises a lower threshold and an upper threshold; anindicator configured to indicate the thrust envelope parameter; and adisplay communicatively connected to the processor and configured todisplay a visual representation of the recommended range.

In an aspect, a method for transitioning between vertical flight andedgewise flight using a transition gauge is provided. The methodincludes identifying, by a propulsor, a thrust envelope parameter of apropulsor of an electric aircraft, determining, by a propulsor, arecommended range as a function of the propulsor parameter, wherein therecommended range comprises a lower threshold and an upper threshold,indicating, by an indicator, a thrust envelope parameter, anddisplaying, by a display communicatively connected to the processor, avisual representation of the recommended range.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram of an exemplary embodiment of a propulsorgauge in accordance with one or more embodiments of the presentdisclosure;

FIG. 2 is schematic diagram of the propulsor gauge in accordance withone or more embodiments of the present disclosure;

FIG. 3 is a schematic diagram of an exemplary embodiment of an electricaircraft in accordance with one or more embodiments of the presentdisclosure;

FIG. 4 is an exemplary embodiment of a machine-learning system inaccordance with one or more embodiments of the present disclosure;

FIG. 5 is a block diagram of an exemplary embodiment of a flightcontroller system in accordance with one or more embodiments of thepresent disclosure, and

FIG. 6 is a block diagram of an exemplary embodiment of a computingsystem in accordance with one or more embodiments of the presentdisclosure.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the present invention. It will be apparent, however,that the present invention may be practiced without these specificdetails. As used herein, the word “exemplary” or “illustrative” means“serving as an example, instance, or illustration.” Any implementationdescribed herein as “exemplary” or “illustrative” is not necessarily tobe construed as preferred or advantageous over other implementations.All of the implementations described below are exemplary implementationsprovided to enable persons skilled in the art to make or use theembodiments of the disclosure and are not intended to limit the scope ofthe disclosure, which is defined by the claims.

Now referring to the drawings, FIG. 1 illustrates an exemplaryembodiment of a propulsor transition gauge 100 in accordance with one ormore embodiments of the present disclosure. In one or more embodiments,propulsor gauge 100 (also referred to in this disclosure as a “gauge”,“transition gauge”, or “propulsor gauge”) may include a digital primaryflight display (PFD). As discussed further below, gauge 100 isconfigured to guide a pilot through a transition of an aircraftpropulsion assembly, such as a propulsor, during flight. For thepurposes of this disclosure, a “transition” is a change of a propulsionassembly between a vertical flight position to a horizontal flightposition. A propulsion assembly may include, for example and withoutlimitation, a propulsor, which may include a rotor or propeller. In someembodiments, gauge 100 may include a tachometer that measures and showsa working speed, such as revolutions per minute (RPM) of one or morepropulsors of an electric aircraft. In one or more embodiments, gauge100 may include a processor 104. Processor 104 may be in communicationwith a computing device or be part of a computing device. Processor 104may include any computing device as described in this disclosure,including without limitation a processor (e.g., processor 104), controlcircuit, microcontroller, microprocessor, digital signal processor(DSP), system on a chip (SoC), and the like. Computing device mayinclude a computer system with one or more processors (e.g., CPUs), agraphics processing unit (GPU), or any combination thereof. In someembodiments, computing device may include a memory 108. In otherembodiments, computing device and/or processor may be communicativelyconnected to and separately from memory 108. Memory 108 may include amemory, such as a main memory and/or a static memory, as discussedfurther in this disclosure below. In some embodiments, computing devicemay include a display 112, as discussed further below in the disclosure.In other embodiments, computing device and/or processor may be separateand communicatively connected to display 112. In one or moreembodiments, computing device may include, be included in, and/orcommunicate with a mobile device, such as a mobile telephone,smartphone, tablet, and the like. In one or more embodiments, computingdevice may include an electronic flight instrument system (EFIS) in acockpit of an electric aircraft. Computing device may be part of or incommunication with a glass cockpit or a round dial cockpit. Computingdevice may include a single computing device operating independently, ormay include two or more computing devices operating in concert, inparallel, sequentially, or the like. Two or more computing devices maybe included together in a single computing device or in two or morecomputing devices. Computing device may interface or communicate withone or more additional devices, as described below in further detail,via a network interface device. Network interface device may be utilizedfor connecting computing device to one or more of a variety of networks,and one or more devices. Examples of a network interface device include,but are not limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, any combination thereof, and thelike. Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two computing devices, and any combinations thereof. A networkmay employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, softwareetc.) may be communicated to and/or from a computer and/or a computingdevice. Computing device 104 may include but is not limited to, forexample, a computing device or cluster of computing devices in a firstlocation and a second computing device or cluster of computing devicesin a second location. Computing device may include one or more computingdevices dedicated to data storage, security, distribution of traffic forload balancing, and the like. Computing device may distribute one ormore computing tasks, as described below, across a plurality ofcomputing devices of computing device, which may operate in parallel, inseries, redundantly, or in any other manner used for distribution oftasks or memory between computing devices. Computing device may beimplemented using a “shared nothing” architecture in which data iscached at the worker, in an embodiment, this may enable scalability ofgauge and/or corresponding computing device.

With continued reference to FIG. 1 , processor 104 may be designedand/or configured to perform any method, method step, or sequence ofmethod steps in any embodiment described in this disclosure, in anyorder and with any degree of repetition. For instance, processor 104 maybe configured to perform a single step or a sequence of steps repeatedlyuntil a desired outcome or commanded outcome is achieved. Repetition ofa step or a sequence of steps may be performed iteratively and/orrecursively using outputs of previous repetitions as inputs tosubsequent repetitions, aggregating inputs, and/or outputs ofrepetitions to produce an aggregate result, reduction or decrement ofone or more variables such as global variables, and/or division of alarger processing task into a set of iteratively addressed smallerprocessing tasks. Processor 104 may perform any step or sequence ofsteps as described in this disclosure in parallel, such assimultaneously and/or substantially simultaneously performing a step twoor more times using two or more parallel threads, processor cores, orthe like. Division of tasks between parallel threads and/or processesmay be performed according to any protocol suitable for division oftasks between iterations. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in whichsteps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

With continued reference to FIG. 1 , gauge 100 includes memory 108.Memory 108 may be communicatively connected to processor 104 and may beconfigured to store information and/or data related to gauge 100, suchas propulsor data, environment data, electric aircraft data, and thelike. Memory 108 may include one or more memory devices to store dataand information and/or datum related to a slight transition of electricaircraft 120. Memory 108 may include various types of memory for suchinformation storage, such as EEPROM (Electrically-Erasable Read-OnlyMemory), flash memory, volatile memory, non-volatile memory, RAM(Random-Access Memory), ROM (Read-Only Memory), a disk drive, and thelike. In various embodiments, processor 104 may be configured to executesoftware instructions stored on memory 108 to perform various methods,processes, or operations in the manner described in this disclosure. Inone or more embodiments, memory 108 is communicatively connected toprocessor 104 and configured to contain instructions configuringprocessor 104 to determine a recommended range 116, as discussed furtherbelow in this disclosure. In one or more embodiments, recommended range116 may include a recommended range for, for example, one or more thrustparameters. In some embodiments, recommended range 116 may include arecommended range for a velocity of electric aircraft 120. In someembodiments, recommended range 116 may include a recommended range foran attitude of electric aircraft 120. Memory component 140 may beconfigured to store information and data related to recommended range116, electric aircraft 120, parameters, and the like. In one or moreembodiments, memory 108 may include a storage device, as describedfurther in this disclosure below. In one or more embodiments, memory 108may store a database such as a database related to electric aircraft.Memory 108 may store upper and lower bounds of a band of gauge 100 in alookup table, as discussed further below in this disclosure. In one ormore embodiments, recommended range 116 may be determined usingmachine-learning. For instance, and without limitation, a generatedmachine-learning model may determine gradations of recommended range116. A gradation may include various recommended parameters based ondesirability for reducing load on electric aircraft while maintaininglift during flight transition. For example, and without limitation, afirst gradation may represent an optimal or targeted zone, where anoptimal zone is an ideal zone that a user should stay within, such as arecommended range 116. A second gradation may include a buffer zone,which may include an upper bound and a lower bound, as discussed furtherin this disclosure. An upper bound may be, for example and withoutlimitation, related to structural limitations. For instance, and withoutlimitation, as electric aircraft moves forward in horizontal flight,loads get added to rotational velocity of lift propulsors, which resultin high loads. Furthermore, lift propulsors may experience a rollingmoment when aircraft is moving horizontally as a result in difference inlift generated by the proceeding propulsor and the receding propulsor. Alower bound may be, for example and without limitation, related to liftand the avoidance of downwash from lift propulsors. For instance, andwithout limitation, if lift propulsors begin moving in their ownpreviously created downwash, which results in turbulence and a decreasein lift, then electric aircraft will begin to descend and/or fall. Lowerbound and upper bound may not be recommended like an optimal zone, but auser may allow a thrust envelope parameter to reside in lower or upperbound without hazardous repercussions, such as dangerous loads onpropulsor or insufficient lift due to downwash. Upper and lower boundprovide. Machine-learning model may modify gradations based on variousfactors and/or parameters, such as external factors like forward speed,exterior temperature, motor temperature, and the like. For example, andwithout limitation, an upper bound for an RPM parameter may be loweredif speed is increase or if an external and/or environmental ambienttemperature is high. In another example, and without limitations, alower bound may be increased if fixed wings of an electric aircraftbegin to generate lift. In one or more embodiments, gradations may bedetermined using machine-learning, as previously mentioned in thisdisclosure. In other embodiments, gradations may be determined using,for example, a lookup table. A lookup table may be stored in a database,such as in a memory of computing device, or may be retrieved from, forexample, a third-party application. Computing device may retrieve orreceive information from lookup table to display gradations, such asrecommended range 116.

In one or more embodiments, gauge 100 may be used on any aircraftcapable of transitioning a flight mode (e.g., from hover to conventionalflight, or vice versa). For example, aircraft may include an electricvertical takeoff and landing (eVTOL), a VTOL, a tilt rotor, a tiltwing,and/or a helicopter. Aircraft may include an electric aircraft, whichmay be any aircraft powered by electricity, such as one or more electricmotors and/or battery systems. In some embodiments, electric aircraftmay be powered solely by electricity. In other embodiments, electricaircraft may be partially powered by electricity, such as ahybrid-electric aircraft. Gauge 100 may assist a pilot in a transitionof an aircraft by providing a recommended range of operation, such as arecommended range for an RPM of a propulsor of aircraft, which must bemaintained by a thrust envelope parameter, such as a current RPM of apropulsor, to safely and readily transition aircraft from one mode offlight to another mode of flight, as discussed in further detail below.Transition between flight modes of an electric aircraft may beconsistent with disclosure of U.S. patent application Ser. No.17/825,371 filed on May 26, 2022, and titled “AN APPARATUS FOR GUIDING ATRANSITION BETWEEN FLIGHT MODES OF AN ELECTRIC AIRCRAFT”, the entiretyof which is incorporated by reference herein in its entirety.

With continued reference to FIG. 1 , processor 104 is configured toidentify a thrust envelope parameter 132 of propulsor 124. For thepurposes of this disclosure, a “thrust envelope parameter” is acharacteristic of a propulsor, such as a propeller, in real time. Thrustenvelope parameter 132 may include a rotational speed or revolutions perminute (RPM) of propulsor 124 and/or a corresponding motor of propulsor124. Processor 104 may identify thrust envelope parameter 132 byreceiving a detected measurement from a sensor 128, as discussed furtherin this disclosure. Though thrust envelope parameter is described as anRPM of a motor of electric aircraft during flight transition, asunderstood by one of ordinary skill in the art, thrust envelopeparameter may be applicable to other parameters of flight of electricaircraft, such as an angle of attack, thrust, torque, power consumption,angular velocity, climb rate, structural limitations of electricaircraft, environmental and/or external limitations surrounding electricaircraft, and the like. In one or more embodiments, thrust envelopeparameter may include a parameter used by an electric aircraft totransition between vertical flight and horizontal flight and to maintaina correct amount of lift by the electric aircraft during the transitionof operation modes.

In one or more embodiments, sensor 128 may include an encoder. Adetected measurement may include a direct reading of a speed or RPM ofpropulsor 124. Detected measurement may include measurements of othercharacteristics of propulsor 124 that may be used by processor 104 tocalculate thrust envelope parameter 132. For example, a measurement ofan actuation of a pilot control, such as a pushing of a throttle lever,may be used to identify a thrust envelope parameter. A pilot control mayinclude, for example and without limitation, a wheel, pedal, button,switch, knob, lever, stick, or any other device and or mechanism used bya pilot to control movement of electric aircraft 120 through a medium.Gauge may indicate a rotational speed of one or more propulsor and/ormotor or shaft operatively connected to propulsor 124. Thrust envelopeparameter 132 may include lift, torque, motor current, motor voltage,propulsor angle and/or tilt, and the like. In some cases, thrustenvelope parameter 132 includes a unitless or proportional parameter,such as speed or throttle. In other cases, thrust envelope parameter 132may include a pilot input position, such as an angular position on liftlever or throttle wheel, as previously mentioned above. In one or moreembodiments, sensor 128 may include one or more shaft (rotary type)encoder, photoelectric (optical type) sensor, and/or magnetic rotationalspeed (proximity type) sensor to detect an RPM or rotational speed ofmotor and/or propulsor 124 of electric aircraft 120.

Still referring to FIG. 1 , a sensor 128 may be communicativelyconnected to processor 104. Sensor 128 may be configured to detect andtransmit a thrust envelope parameter 132, condition data 136, and thelike. For the purposes of this disclosure, condition data 136 is anyfactor or characteristic of electric aircraft that may affect atransition of flight of electric aircraft. In one or more embodiments, acondition datum may include an RPM, angle of attack, thrust, torque,power consumption, angular velocity, climb rate, structural limitationsof electric aircraft, environmental and/or external limitationssurrounding electric aircraft, such as wind or an ambient temperature ofan external environment of electric aircraft, a power source condition,and the like. In one or more embodiments, thrust envelope parameter mayinclude a parameter indicating a used by an electric aircraft totransition between vertical flight and horizontal flight and to maintaina correct amount of lift by the electric aircraft during the transitionof operation modes. In one or more embodiments, thrust envelopeparameter and condition data may be inputted by, for example, a user, ordetected by, for example, a sensor, such as sensor 128. Sensor 128 mayinclude one or more sensors. For example, sensor 128 may include asensor array or a plurality of individual sensors.

In one or more embodiments, sensor 128 may include an encoder. In one ormore embodiments, gauge 100 and/or electric aircraft 120 may include anencoder. An encoder may be configured to detect and determine a motionof motor of propulsor 124. For example, and without limitation, encodermay be a rotary encoder. In one or more exemplary embodiments, encoderis configured to determine a motion of motor and/or propulsor, such as aspeed in revolutions per minute of motor. Encoder is configured totransmit an output signal, which may include feedback, to processor 104.

Still referring to FIG. 1 , sensor 128 may include a motion sensor. A“motion sensor,” for the purposes of this disclosure, refers to a deviceor component configured to detect physical movement of an object orgrouping of objects. One of ordinary skill in the art would appreciate,after reviewing the entirety of this disclosure, that motion may includea plurality of types including but not limited to: spinning, rotating,oscillating, gyrating, jumping, sliding, reciprocating, or the like.Sensor 128 may include, torque sensor, gyroscope, accelerometer,magnetometer, inertial measurement unit (IMU), pressure sensor, forcesensor, proximity sensor, displacement sensor, vibration sensor, or thelike. For example, without limitation, sensor 128 may include agyroscope that is configured to detect a current aircraft orientation,such as roll angle.

In one or more embodiments, sensor 128 may include a plurality ofweather sensors. In one or more embodiments, sensor 128 may include awind sensor. In some embodiments, a wind sensor may be configured tomeasure a wind datum. A “wind datum” may include data of wind forcesacting on an aircraft. Wind datum may include wind strength, direction,shifts, duration, or the like. For example, and without limitations,sensor 128 may include an anemometer. An anemometer may be configured todetect a wind speed. In one or more embodiments, the anemometer mayinclude a hot wire, laser doppler, ultrasonic, and/or pressureanemometer. In some embodiments, sensor 128 may include a pressuresensor. “Pressure,” for the purposes of this disclosure and as would beappreciated by someone of ordinary skill in the art, is a measure offorce required to stop a fluid from expanding and is usually stated interms of force per unit area. The pressure sensor that may be includedin sensor 200 may be configured to measure an atmospheric pressureand/or a change of atmospheric pressure. In some embodiments, thepressure sensor may include an absolute pressure sensor, a gaugepressure sensor, a vacuum pressure sensor, a differential pressuresensor, a sealed pressure sensor, and/or other unknown pressure sensorsor alone or in a combination thereof. In one or more embodiments, apressor sensor may include a barometer. In some embodiments, a pressuresensor may be used to indirectly measure fluid flow, speed, water level,and altitude. In some embodiments, the pressure sensor may be configuredto transform a pressure into an analogue electrical signal. In someembodiments, the pressure sensor may be configured to transform apressure into a digital signal.

In one or more embodiments, sensor 128 may include an altimeter that maybe configured to detect an altitude of aircraft 104. In one or moreembodiments, sensor 128 may include a moisture sensor. “Moisture,” asused in this disclosure, is the presence of water, this may includevaporized water in air, condensation on the surfaces of objects, orconcentrations of liquid water. Moisture may include humidity.“Humidity,” as used in this disclosure, is the property of a gaseousmedium (almost always air) to hold water in the form of vapor. In one ormore embodiments, sensor 128 may include an altimeter. The altimeter maybe configured to measure an altitude. In some embodiments, the altimetermay include a pressure altimeter. In other embodiments, the altimetermay include a sonic, radar, and/or Global Positioning System (GPS)altimeter. In some embodiments, sensor 128 may include a meteorologicalradar that monitors weather conditions. In some embodiments, sensor 128may include a ceilometer. The ceilometer may be configured to detect andmeasure a cloud ceiling and cloud base of an atmosphere. In someembodiments, the ceilometer may include an optical drum and/or laserceilometer. In some embodiments, sensor 128 may include a rain gauge.The rain gauge may be configured to measure precipitation. Precipitationmay include rain, snow, hail, sleet, or other precipitation forms. Insome embodiments, the rain gauge may include an optical, acoustic, orother rain gauge. In some embodiments, sensor 128 may include apyranometer. The pyranometer may be configured to measure solarradiation. In some embodiments, the pyranometer may include a thermopileand/or photovoltaic pyranometer. The pyranometer may be configured tomeasure solar irradiance on a planar surface. In some embodiments,sensor 128 may include a lightning detector. The lightning detector maybe configured to detect and measure lightning produced by thunderstorms.In some embodiments, sensor 128 may include a present weather sensor(PWS). The PWS may be configured to detect the presence of hydrometeorsand determine their type and intensity. Hydrometeors may include aweather phenomenon and/or entity involving water and/or water vapor,such as, but not limited to, rain, snow, drizzle, hail and sleet. Insome embodiments, sensor 128 may include an inertia measurement unit(IMU). The IMU may be configured to detect a change in specific force ofa body.

In one or more embodiments, sensor 128 may include a local sensor. Alocal sensor may be any sensor mounted to aircraft 104 that sensesobjects or phenomena in the environment around aircraft 104. Localsensor may include, without limitation, a device that performs radiodetection and ranging (RADAR), a device that performs lidar, a devicethat performs sound navigation ranging (SONAR), an optical device suchas a camera, electro-optical (EO) sensors that produce images that mimichuman sight, or the like. In one or more embodiments, sensor 128 mayinclude a navigation sensor. For example, and without limitation, anavigation system of aircraft 104 may be provided that is configured todetermine a geographical position of aircraft 104 during flight. Thenavigation may include a Global Positioning System (GPS), an AttitudeHeading and Reference System (AHRS), an Inertial Reference System (IRS),radar system, and the like.

In one or more embodiments, sensor 128 may include electrical sensors.Electrical sensors may be configured to measure voltage across acomponent, electrical current through a component, and resistance of acomponent. In one or more embodiments, sensor 128 may includethermocouples, thermistors, thermometers, passive infrared sensors,resistance temperature sensors (RTD's), semiconductor based integratedcircuits (IC), a combination thereof or another undisclosed sensor type,alone or in combination. Temperature, for the purposes of thisdisclosure, and as would be appreciated by someone of ordinary skill inthe art, is a measure of the heat energy of a system. Temperature, asmeasured by any number or combinations of sensors present within sensor128, may be measured in Fahrenheit (° F.), Celsius (° C.), Kelvin (° K),or another scale alone or in combination. The temperature measured bysensors may comprise electrical signals which are transmitted to theirappropriate destination wireless or through a wired connection.

In one or more embodiments, sensor 128 may include a sensor suite whichmay include a plurality of sensors that may detect similar or uniquephenomena. For example, in a non-limiting embodiment, sensor suite mayinclude a plurality of accelerometers, a mixture of accelerometers andgyroscope. System 100 may include a plurality of sensors in the form ofindividual sensors or a sensor suite working in tandem or individually.A sensor suite may include a plurality of independent sensors, asdescribed in this disclosure, where any number of the described sensorsmay be used to detect any number of physical or electrical quantitiesassociated with an aircraft. Independent sensors may include separatesensors measuring physical or electrical quantities that may be poweredby and/or in communication with circuits independently, where each maysignal sensor output to a control circuit such as a user graphicalinterface. In an embodiment, use of a plurality of independent sensorsmay result in redundancy configured to employ more than one sensor thatmeasures the same phenomenon, those sensors being of the same type, acombination of, or another type of sensor not disclosed, so that in theevent one sensor fails, the ability to detect phenomenon is maintained.Sensor 128 may be configured to detect pilot input from pilot controland/or controller 112. In one or more embodiments, a pilot control mayinclude buttons, switches, or other binary inputs in addition to, oralternatively than digital controls about which a plurality of inputsmay be received. Pilot control may be configured to receive pilot input.Pilot input may include a physical manipulation of a control like apilot using a hand and arm to push or pull a lever, or a pilot using afinger to manipulate a switch. Pilot input may include a voice commandby a pilot to a microphone and computing system consistent with theentirety of this disclosure. One of ordinary skill in the art, afterreviewing the entirety of this disclosure, would appreciate that this isa non-exhaustive list of components and interactions thereof that mayinclude, represent, or constitute, at least aircraft command. A pilotcontrol may include a throttle lever, inceptor stick, collective pitchcontrol, steering wheel, brake pedals, pedal controls, toggles,joystick. One of ordinary skill in the art, upon reading the entirety ofthis disclosure would appreciate the variety of pilot input controlsthat may be present in an electric aircraft consistent with the presentdisclosure. Inceptor stick may be consistent with disclosure of inceptorstick in U.S. patent application Ser. No. 17/001,845 and titled “A HOVERAND THRUST CONTROL ASSEMBLY FOR DUAL-MODE AIRCRAFT”, which isincorporated herein by reference in its entirety. Collective pitchcontrol may be consistent with disclosure of collective pitch control inU.S. patent application Ser. No. 16/929,206 and titled “HOVER AND THRUSTCONTROL ASSEMBLY FOR DUAL-MODE AIRCRAFT”, which is incorporated hereinby reference in its entirety. The manipulation of a pilot control mayconstitute an aircraft command. A pilot control may be physicallylocated in the cockpit of an aircraft or remotely located outside of theaircraft in another location communicatively connected to at least aportion of the aircraft. A pilot input and/or control may include acollective, inceptor, foot bake, steering and/or control wheel, controlstick, pedals, throttle levers, or the like. “Communicativelyconnected,” for the purposes of this disclosure, is a process wherebyone device, component, or circuit is able to receive data from and/ortransmit data to another device, component, or circuit; communicativeconnecting may be performed by wired or wireless electroniccommunication, either directly or by way of one or more interveningdevices or components. In an embodiment, communicatively connectingincludes electrically coupling an output of one device, component, orcircuit to an input of another device, component, or circuit.Communicatively connecting may be performed via a bus or other facilityfor intercommunication between elements of a computing device.Communicatively connecting may include indirect connections via“wireless” connection, low power wide area network, radio communication,optical communication, magnetic, capacitive, or optical coupling, or thelike.

With continued reference to FIG. 1 , system 100 includes sensor 128communicatively connected to aircraft 104. Sensor 128 may be configuredto detect, for example, a thrust envelope parameter 132 and/or acondition data 136. Condition datum, or the purposes of this disclosure,may include information and/or data related to actual motion, forces,moments, and/or torques acting on aircraft and/or describing anenvironmental phenomenon in the real world surrounding electricaircraft. For example, and without limitation, condition data 136 mayinclude geographical data, wind data, electric aircraft specificationdata (e.g., aircraft type, weight, battery type, battery state ofcharge, and the like). Condition data 136 may also be used by processor104, alone or in addition to thrust envelope parameter, to determinerecommended range 116.

In various embodiments, sensor 128 may include an inertial measurementunit. An “inertial measurement unit,” for the purposes of thisdisclosure, is an electronic device that measures and reports a body'sspecific force, angular rate, and orientation of the body, using acombination of accelerometers, gyroscopes, and magnetometers, in variousarrangements and combinations.

In various embodiments, sensor 128 may include a plurality of sensors inthe form of individual sensors or a sensor array. Sensor 128 may includea plurality of independent sensors, where any number of the describedsensors may be used to detect any number of physical or electricalphenomenon associated with electric aircraft 120. Independent sensorsmay include separate sensors measuring physical or electrical quantitiesthat may be powered by and/or in communication with circuitsindependently, where each may signal sensor output to a control circuitsuch as a user graphical interface. In an embodiment, use of a pluralityof independent sensors may result in redundancy configured to employmore than one sensor that measures the same phenomenon, those sensorsbeing of the same type, a combination of, or another type of sensor notdisclosed, so that in the event one sensor fails, the ability of sensor128 to detect phenomenon may be maintained.

In various embodiments, sensor 128 may be communicatively connected toprocessor 104, memory 108, display 112, a pilot input, and/or a flightcontroller so that sensor 128 may transmit and/or receive signals.Signals may include electrical, electromagnetic, visual, audio, radiowaves, or another undisclosed signal type alone or in combination. Inone or more embodiments, processor 104 may receive an attitude ofelectric aircraft, such as a yaw, pitch, or roll from sensor 128. In oneor more embodiments, processor 104 may be aproportional-integral-derivative (PID) controller. In other embodiments,processor 104 may be a flight controller, which is described furtherbelow.

With continued reference to FIG. 1 , processor 104 is configured todetermine a recommended range 116 as a function of condition parameter132. In one or more embodiments, thrust envelope parameter 132 mayinclude a current RPM of one or more propellers of electric aircraft. Inother embodiments, processor 104 may be configured to determinerecommended range 116 as a function of condition parameter 132 and/orcondition data 136. Condition parameters may include environmentalconditions, electric aircraft characteristics, and the like. Forexample, and without limitation, condition parameter 136 may include awind characteristic experienced by electric aircraft. In anotherexample, and without limitation, condition data 136 may include an angleand/or tilt of each propeller of electric aircraft. Condition data 136may be inputted by a user or retrieved form a database or memory 108.For example, and without limitation, a lookup table and or database maybe provided by a user or third-party application that includes electricaircraft specifications, such as, for example, information related to atype of electric aircraft, a battery amperage of electric aircraft, abattery voltage of electric aircraft, a propulsion assembly motor type,a propulsor type, a weight of electric aircraft 120, and the like. In ormore embodiments, recommended range 116 may be determined using amachine-learning model, as discussed further below in this disclosure.Though recommended range 116 is described as a recommendation for a userstaying in a predetermined RPM during flight transition, as understoodby one of ordinary skill in the art, recommended range may be applicableto other parameters of flight of electric aircraft, such as an angle ofattack, climb rate, structural limitations, environmental limitations,and the like. Other parameters may be shown in display 112 alone ortogether, as shown in FIG. 2 with a first parameter 220, a secondparameter 224, and a third parameter 228.

In one or more embodiments, recommended range 116 may be updated. Forexample, and without limitation, an initial recommended range 116 a maybe adjusted to create an updated recommended range 116 b based on anupdated thrust envelope parameter and/or an updated condition data, asshown in FIG. 2 . Recommended range may be continuously adjusted inreal-time based on real-time thrust envelope parameters an/or conditiondata.

Now referring to FIG. 2 , an exemplary embodiment of gauge 100 is shown.In one or more embodiments, gauge 100 includes display 112, which isconfigured to display, present, indicate, and/or otherwise visuallyand/or verbally convey data and/or information related to a flighttransition of electric aircraft 120. For example, and withoutlimitation, display 112 may show recommended range 116, thrust envelopeparameter 132, condition data 136, and the like. In another example, andwithout limitation, information generated by processor 104 or sensor 128may be shown by display 112. In other examples, and without limitation,information stored in and provided by memory 108 may be displayed ondisplay 108. In various embodiments, display 108 may be implemented withan electronic display screen and/or monitor. Exemplary embodiments ofelectronic display screen may include a cathode ray tube (CRT),light-emitting diode (LED), liquid-crystal display (LCD), an opaquescreen, and the like. In various embodiments, display 108 may beimplemented with a projection screen and/or display. For example, andwithout limitation, display 108 may include a head-up display, aprojector screen, a pico-projection display, a retinal display, and thelike. In one or more embodiments, display 112 may include a monochromeor color display. Display 112 may be suitable for presenting auser-viewable image of one or more visual representations related togenerated and/or provided information discussed in this disclosure. Insome embodiments, gauge 100 may also be shown on an existing display ofan external and/or remote device, such as a remote computing device,laptop, desktop, mobile phone, tablet, electric aircraft 120 informationdisplay system, or any other devices that may receive flight transitioninformation from processor, sensor, memory, a remote computing device,and the like, to present flight transition information to a user. Insome embodiments, display 112 may receive and display data and/orinformation converted and/or generated from processor 104. In otherembodiments, display 108 may receive and display collected data and/orinformation directly from sensor 128. In other embodiments, display 112may receive and show data and/or information stored and retrieved frommemory 108. Data and information from memory 108 may be transferred frommemory 108 via processor 104. Display 112 may be configured to present,indicate, or otherwise convey images and or symbols, such as text,related to a flight transition of electric aircraft 120.

With continued reference to FIG. 2 , gauge 100 may include an indicator204 (shown in FIG. 2 ) that represents a thrust envelope parameter 132,such as an RPM of motor and/or propulsor 124 of electric aircraft 120.Gauge 100 may include physical numerals indicating various values and/orunits of a thrust envelope parameter. Indicator 204 may be a physicalcomponent moveably mounted to a backing, such as display 112. Forexample, and without limitation, indicator 204 may include a needlerotatably or slidable attached to display 112 or any other backing. Inanother example, and without limitation, indicator 204 may include oneor more LEDs. In other embodiments, display 112 may show a visualrepresentation of an indicator, such as a needle or other visual markerof a thrust envelope parameter, which indicates a current RPM of one ormore propulsor assemblies of electric aircraft 120. For example, andwithout limitation, gauge 100 may be a display that generates an imagemimicking an analog tachometer and/or measuring instrument. Indicator204 may be coated in a luminescent or reflective paint or material toallow for indicator to be more readily seen by a user, such asself-illuminating tritium or a photoluminescent paint.

With continued reference to FIG. 2 , display may show a real-timerecommended range 116 that may range with real-time detections fromsensors, information from processor 104, and/or inputs from a user, suchas a pilot. A visual representation may be used to indicated recommendedrange 116. Visual representation of recommended range 116 may include aband 208. In some embodiments, band 208 may be a linear region indicatedby colors or lines on display 112. In other embodiments, band 208 mayinclude an arcuate region indicated by colors and/or lines on display112. A position of indicator 204, which represents a thrust envelopeparameter such as, for example, RPM of a motor of propulsor or a thrustof electric aircraft, must be maintained so that indicator 204 isdirected at recommended range, e.g., pointing at a region between alower bound 212 a of recommended region 116 and upper bound 212 b ofrecommended region 116. Lower bound 212 a may represent a lowerthreshold of recommended range 116, and upper bound 212 b may representan upper threshold of recommended range 116. For a safe and propertransition of modes of operation of electric aircraft a user must use apilot control to keep indicator 204 between the upper bound and thelower bound of recommended range 116. In some embodiments, lower andupper bound may be generated to include a safety feature, where astandard deviation of error may be included so that recommended rangeincludes relief for user and/or environmental error.

In one or more embodiments, gauge 100 may include an alert component,which may be activated if a position of indicator 204 is outside ofrecommended range 116, such as position 216 of indicator 204. Alertcomponent may include one or more visual components, audio components,haptic components, and the like. For example, and without limitation,alert component may include a haptic component where a user may feel avibration in a pilot input when a user deviates outside of recommendedrange 116 or is close to deviating outside of recommended range 116. Inanother example, and without limitation, display 112 or a light-emittingdiode (LED) may flash to indicate to a user that the user is deviatingfrom a recommended range. Haptic components may include mechanicalvibrators, piezoelectric components, or other movable components forgenerating motion that alerts a user that a recommended range is beingexceeded. Audio components may include one or more speakers.Light-emitting components may include one or more light bulbs, LEDs, atleast a portion of display 112, and the like.

Sensor 128 may collect data related to a flight transition of electricaircraft 120. Sensor 128 may include one or more sensors. For example,and without limitation, sensor 128 may include a plurality of sensors.Such as a sensor array. Display may display measured and/or detectedparameters from sensor 128. Display 112 may be a touchscreen and, thus,provide controls of gauge 100 using screen. In other embodiments, gauge100 may include a user interface, such as a mechanical interface, withactuated components that are adapted to generate one or more useractuated input control signals. Controls may include one or morebuttons, switches, sliders, joysticks, keyboard, pedals, rotatableknobs, a peripheral device, remote device, and the like, that allow auser to navigate an interface, such as a graphic user interface (GUI),displayed on display 112. For example, and without limitation, a usermay use controls to toggle between various units of displayedinformation (e.g., miles per hour (mph), kilometers per hour (kph),revolutions per minute (RPM), and the like) or various menus of graphicuser interface or various versions of displaying information (e.g., suchas a circular display versus a linear display as shown in FIG. 2 andFIG. 3 , respectively). In one or more embodiments, processor 104 mayprocess detected sensor data from sensor 128. Processor may then presentprocessed sensor data to a user via display 112.

User interface may be adapted to be integrated as part of display 112 tofunction as both a user input device and a display device, such as, forexample, a touch screen device adapted to receive input signals from auser touching different parts of a screen of display 112. Processor maybe configured to sense a user control input signal from user interfaceand respond to sensed control input signals received therefrom.

Referring now to FIG. 3 , an illustration of an exemplary embodiment ofaircraft 120 is shown. In some embodiments, aircraft 120 may include anelectric aircraft. In some embodiments, aircraft 120 may include avertical takeoff and landing aircraft (eVTOL). As used herein, avertical take-off and landing (eVTOL) aircraft is one that may hover,take off, and land vertically. An eVTOL, as used herein, is anelectrically powered aircraft typically using an energy source, of aplurality of energy sources to power the aircraft. In order to optimizethe power and energy necessary to propel the aircraft. eVTOL may becapable of rotor-based cruising flight, rotor-based takeoff, rotor-basedlanding, fixed-wing cruising flight, airplane-style takeoff,airplane-style landing, and/or any combination thereof. Rotor-basedflight, as described herein, is where the aircraft generated lift andpropulsion by way of one or more powered rotors coupled with an engine,such as a “quad copter,” multi-rotor helicopter, or other vehicle thatmaintains its lift primarily using downward thrusting propulsors.Fixed-wing flight, as described herein, is where the aircraft is capableof flight using wings and/or foils that generate life caused by theaircraft's forward airspeed and the shape of the wings and/or foils,such as airplane-style flight.

With continued reference to FIG. 1 , a number of aerodynamic forces mayact upon the electric aircraft 120 during flight. Forces acting on anaircraft 120 during flight may include, without limitation, thrust, theforward force produced by the rotating element of the aircraft 120 andacts parallel to the longitudinal axis. Another force acting uponaircraft 120 may be, without limitation, drag, which may be defined as arearward retarding force which is caused by disruption of airflow by anyprotruding surface of the aircraft 120 such as, without limitation, thewing, rotor, and fuselage. Drag may oppose thrust and acts rearwardparallel to the relative wind. A further force acting upon aircraft 120may include, without limitation, weight, which may include a combinedload of the electric aircraft 120 itself, crew, baggage, and/or fuel.Weight may pull aircraft 120 downward due to the force of gravity. Anadditional force acting on aircraft 120 may include, without limitation,lift, which may act to oppose the downward force of weight and may beproduced by the dynamic effect of air acting on the airfoil and/ordownward thrust from the propulsor of the electric aircraft. Liftgenerated by the airfoil may depend on speed of airflow, density of air,total area of an airfoil and/or segment thereof, and/or an angle ofattack between air and the airfoil. For example, and without limitation,aircraft 120 are designed to be as lightweight as possible. Reducing theweight of the aircraft and designing to reduce the number of componentsis essential to optimize the weight.

Referring still to FIG. 3 , aircraft 120 may include at least a verticalpropulsor 304 and at least a forward propulsor 308. A forward propulsoris a propulsor that propels the aircraft in a forward direction. Forwardin this context is not an indication of the propulsor position on theaircraft; one or more propulsors mounted on the front, on the wings, atthe rear, etc. A vertical propulsor is a propulsor that propels theaircraft in an upward direction; one of more vertical propulsors may bemounted on the front, on the wings, at the rear, and/or any suitablelocation. A propulsor, as used herein, is a component or device used topropel a craft by exerting force on a fluid medium, which may include agaseous medium such as air or a liquid medium such as water. At least avertical propulsor 304 is a propulsor that generates a substantiallydownward thrust, tending to propel an aircraft in a vertical directionproviding thrust for maneuvers such as without limitation, verticaltake-off, vertical landing, hovering, and/or rotor-based flight such as“quadcopter” or similar styles of flight.

With continued reference to FIG. 3 , at least a forward propulsor 308 asused in this disclosure is a propulsor positioned for propelling anaircraft in a “forward” direction; at least a forward propulsor mayinclude one or more propulsors mounted on the front, on the wings, atthe rear, or a combination of any such positions. At least a forwardpropulsor may propel an aircraft forward for fixed-wing and/or“airplane”-style flight, takeoff, and/or landing, and/or may propel theaircraft forward or backward on the ground. At least a verticalpropulsor 104 and at least a forward propulsor 108 includes a thrustelement. At least a thrust element may include any device or componentthat converts the mechanical energy of a motor, for instance in the formof rotational motion of a shaft, into thrust in a fluid medium. At leasta thrust element may include, without limitation, a device using movingor rotating foils, including without limitation one or more rotors, anairscrew or propeller, a set of airscrews or propellers such ascontrarotating propellers, a moving or flapping wing, or the like. Atleast a thrust element may include without limitation a marine propelleror screw, an impeller, a turbine, a pump-jet, a paddle or paddle-baseddevice, or the like. As another non-limiting example, at least a thrustelement may include an eight-bladed pusher propeller, such as aneight-bladed propeller mounted behind the engine to ensure the driveshaft is in compression. Propulsors may include at least a motormechanically coupled to the at least a first propulsor as a source ofthrust. A motor may include without limitation, any electric motor,where an electric motor is a device that converts electrical energy intomechanical energy, for instance by causing a shaft to rotate. At least amotor may be driven by direct current (DC) electric power; for instance,at least a first motor may include a brushed DC at least a first motor,or the like. At least a first motor may be driven by electric powerhaving varying or reversing voltage levels, such as alternating current(AC) power as produced by an alternating current generator and/orinverter, or otherwise varying power, such as produced by a switchingpower source. At least a first motor may include, without limitation,brushless DC electric motors, permanent magnet synchronous at least afirst motor, switched reluctance motors, or induction motors. Inaddition to inverter and/or a switching power source, a circuit drivingat least a first motor may include electronic speed controllers or othercomponents for regulating motor speed, rotation direction, and/ordynamic braking. Persons skilled in the art, upon reviewing the entiretyof this disclosure, will be aware of various devices that may be used asat least a thrust element.

In some embodiments, aircraft 120 may be an eVTOL. In some embodiments,aircraft 120 may have one or more states, or modes, of operation and maytransition between such modes of operation. Aircraft 120 may have ahover state. In a hover state, aircraft 120 may be moving through theair along a vertical path. In some embodiments, aircraft 120 may be in ahover state during liftoff operations. In another embodiment, aircraft120 may be in a hover state in landing operations. In other embodiments,a hover state may be when aircraft 120 maintains an altitude whenairborne. Aircraft 120 may use propulsors, such as vertical propulsor304, to achieve ascent and descent in a hover state. In someembodiments, aircraft 120 may have a fixed-wing flight state. Aircraft120 may be in a fixed-wing flight state during forward, backward, andsideways propulsion. A fixed-wing flight state may include edgewiseflight. In some embodiments, aircraft 120 may have a first set of rotorsfor a hover state. In other embodiments, aircraft 120 may have a secondset of rotors for a fixed-wing flight state. In some embodiments,aircraft 120 may use the same set of propulsors for both hover state andfixed-wing flight states.

In some embodiments, processor 104 may be configured to detect aplurality of flight operations of aircraft 120. In some embodiments,processor 104 may detect a change of aircraft 120 during a transition ofaircraft 120 between a fixed-wing flight state and a hover state. Forexample, and without limitation, a magnetic element may be used tooperably move rotor of propulsor. In some embodiments, rotor 116 andmovement thereof may be as described in U.S. patent application Ser. No.16/938,952, filed Jul. 25, 2020, titled “INTEGRATED ELECTRIC PROPULSIONASSEMBLY”, of which is incorporated by reference herein in its entirety.In some embodiments, a rotor management system may be used to monitor anoperation of electric aircraft such as a management system described inU.S. patent application Ser. No. 17/383,667, filed Jul. 23, 2021,entitled “SYSTEM AND METHOD OF ROTOR MANAGEMENT”, which is incorporatedby reference herein in its entirety. In other embodiments, processor 104may determine a minimal drag axis based on surrounding airflow ofaircraft 120. A drag minimization axis and determining thereof may be asdescribed in U.S. patent application Ser. No. 17/362,454 filed Jun. 29,2021, titled “METHOD OF PROPULSOR MANAGEMENT IN ELECTRIC AIRCRAFT”, ofwhich is incorporated herein by reference in its entirety.

Referring now to FIG. 4 , an exemplary embodiment of a machine-learningmodule 400 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module 400may be implemented in the determination of the flight states of theelectric aircraft. Machine-learning module 400 may communicated with theflight controller to determine a minimal drag axis for the electricaircraft. Machine-learning module may perform determinations,classification, and/or analysis steps, methods, processes, or the likeas described in this disclosure using machine learning processes. A“machine learning process,” as used in this disclosure, is a processthat automatedly uses training data 404 to generate an algorithm thatwill be performed by a computing device/module to produce outputs 408given data provided as inputs 412; this is in contrast to a non-machinelearning software program where the commands to be executed aredetermined in advance by a user and written in a programming language.

Still referring to FIG. 4 , “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 404 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 404 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 404 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 404 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 404 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 404 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data404 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 4 ,training data 404 may include one or more elements that are notcategorized; that is, training data 404 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 404 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 404 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 404 used by machine-learning module 400 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure. As a non-limiting illustrativeexample flight elements and/or pilot signals may be inputs, wherein anoutput may be an autonomous function.

Further referring to FIG. 4 , training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 416. Training data classifier 416 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 400 may generate aclassifier using a classification algorithm, defined as a processeswhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 404. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers. As a non-limiting example, trainingdata classifier 416 may classify elements of training data tosub-categories of flight elements such as torques, forces, thrusts,directions, and the like thereof.

Still referring to FIG. 4 , machine-learning module 400 may beconfigured to perform a lazy-learning process 420 and/or protocol, whichmay alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data 404. Heuristicmay include selecting some number of highest-ranking associations and/ortraining data 404 elements. Lazy learning may implement any suitablelazy learning algorithm, including without limitation a K-nearestneighbors algorithm, a lazy naïve Bayes algorithm, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various lazy-learning algorithms that may be applied togenerate outputs as described in this disclosure, including withoutlimitation lazy learning applications of machine-learning algorithms asdescribed in further detail below.

Alternatively or additionally, and with continued reference to FIG. 4 ,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 424. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above, and stored in memory; aninput is submitted to a machine-learning model 424 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 424 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 404set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 4 , machine-learning algorithms may include atleast a supervised machine-learning process 428. At least a supervisedmachine-learning process 428, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude flight elements and/or pilot signals as described above asinputs, autonomous functions as outputs, and a scoring functionrepresenting a desired form of relationship to be detected betweeninputs and outputs; scoring function may, for instance, seek to maximizethe probability that a given input and/or combination of elements inputsis associated with a given output to minimize the probability that agiven input is not associated with a given output. Scoring function maybe expressed as a risk function representing an “expected loss” of analgorithm relating inputs to outputs, where loss is computed as an errorfunction representing a degree to which a prediction generated by therelation is incorrect when compared to a given input-output pairprovided in training data 404. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variouspossible variations of at least a supervised machine-learning process428 that may be used to determine relation between inputs and outputs.Supervised machine-learning processes may include classificationalgorithms as defined above.

Further referring to FIG. 4 , machine learning processes may include atleast an unsupervised machine-learning processes 432. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 4 , machine-learning module 400 may be designedand configured to create a machine-learning model 424 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 4 , machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includeGaussian processes such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

Now referring to FIG. 5 , an exemplary embodiment 500 of a flightcontroller 504 is illustrated. (Flight controller 124 of FIG. 1 and FIG.2 may be the same as or similar to flight controller 504.) As used inthis disclosure a “flight controller” is a computing device of aplurality of computing devices dedicated to data storage, security,distribution of traffic for load balancing, and flight instruction.Flight controller 504 may include and/or communicate with any computingdevice as described in this disclosure, including without limitation amicrocontroller, microprocessor, digital signal processor (DSP) and/orsystem on a chip (SoC) as described in this disclosure. Further, flightcontroller 504 may include a single computing device operatingindependently, or may include two or more computing device operating inconcert, in parallel, sequentially or the like; two or more computingdevices may be included together in a single computing device or in twoor more computing devices. In embodiments, flight controller 504 may beinstalled in an aircraft, may control the aircraft remotely, and/or mayinclude an element installed in the aircraft and a remote element incommunication therewith.

In an embodiment, and still referring to FIG. 5 , flight controller 504may include a signal transformation component 508. As used in thisdisclosure a “signal transformation component” is a component thattransforms and/or converts a first signal to a second signal, wherein asignal may include one or more digital and/or analog signals. Forexample, and without limitation, signal transformation component 508 maybe configured to perform one or more operations such as preprocessing,lexical analysis, parsing, semantic analysis, and the like thereof. Inan embodiment, and without limitation, signal transformation component508 may include one or more analog-to-digital convertors that transforma first signal of an analog signal to a second signal of a digitalsignal. For example, and without limitation, an analog-to-digitalconverter may convert an analog input signal to a 10-bit binary digitalrepresentation of that signal. In another embodiment, signaltransformation component 508 may include transforming one or morelow-level languages such as, but not limited to, machine languagesand/or assembly languages. For example, and without limitation, signaltransformation component 508 may include transforming a binary languagesignal to an assembly language signal. In an embodiment, and withoutlimitation, signal transformation component 508 may include transformingone or more high-level languages and/or formal languages such as but notlimited to alphabets, strings, and/or languages. For example, andwithout limitation, high-level languages may include one or more systemlanguages, scripting languages, domain-specific languages, visuallanguages, esoteric languages, and the like thereof. As a furthernon-limiting example, high-level languages may include one or morealgebraic formula languages, business data languages, string and listlanguages, object-oriented languages, and the like thereof.

Still referring to FIG. 5 , signal transformation component 508 may beconfigured to optimize an intermediate representation 512. As used inthis disclosure an “intermediate representation” is a data structureand/or code that represents the input signal. Signal transformationcomponent 508 may optimize intermediate representation as a function ofa data-flow analysis, dependence analysis, alias analysis, pointeranalysis, escape analysis, and the like thereof. In an embodiment, andwithout limitation, signal transformation component 508 may optimizeintermediate representation 512 as a function of one or more inlineexpansions, dead code eliminations, constant propagation, looptransformations, and/or automatic parallelization functions. In anotherembodiment, signal transformation component 508 may optimizeintermediate representation as a function of a machine dependentoptimization such as a peephole optimization, wherein a peepholeoptimization may rewrite short sequences of code into more efficientsequences of code. Signal transformation component 508 may optimizeintermediate representation to generate an output language, wherein an“output language,” as used herein, is the native machine language offlight controller 504. For example, and without limitation, nativemachine language may include one or more binary and/or numericallanguages.

In an embodiment, and without limitation, signal transformationcomponent 508 may include transform one or more inputs and outputs as afunction of an error correction code. An error correction code, alsoknown as error correcting code (ECC), is an encoding of a message or lotof data using redundant information, permitting recovery of corrupteddata. An ECC may include a block code, in which information is encodedon fixed-size packets and/or blocks of data elements such as symbols ofpredetermined size, bits, or the like. Reed-Solomon coding, in whichmessage symbols within a symbol set having q symbols are encoded ascoefficients of a polynomial of degree less than or equal to a naturalnumber k, over a finite field F with q elements; strings so encoded havea minimum hamming distance of k+1, and permit correction of (q−k−1)/2erroneous symbols. Block code may alternatively or additionally beimplemented using Golay coding, also known as binary Golay coding,Bose-Chaudhuri, Hocquenghuem (BCH) coding, multidimensional parity-checkcoding, and/or Hamming codes. An ECC may alternatively or additionallybe based on a convolutional code.

In an embodiment, and still referring to FIG. 5 , flight controller 504may include a reconfigurable hardware platform 516. A “reconfigurablehardware platform,” as used herein, is a component and/or unit ofhardware that may be reprogrammed, such that, for instance, a data pathbetween elements such as logic gates or other digital circuit elementsmay be modified to change an algorithm, state, logical sequence, or thelike of the component and/or unit. This may be accomplished with suchflexible high-speed computing fabrics as field-programmable gate arrays(FPGAs), which may include a grid of interconnected logic gates,connections between which may be severed and/or restored to program inmodified logic. Reconfigurable hardware platform 516 may be reconfiguredto enact any algorithm and/or algorithm selection process received fromanother computing device and/or created using machine-learningprocesses.

Still referring to FIG. 5 , reconfigurable hardware platform 516 mayinclude a logic component 520. As used in this disclosure a “logiccomponent” is a component that executes instructions on output language.For example, and without limitation, logic component may perform basicarithmetic, logic, controlling, input/output operations, and the likethereof. Logic component 520 may include any suitable processor, such aswithout limitation a component incorporating logical circuitry forperforming arithmetic and logical operations, such as an arithmetic andlogic unit (ALU), which may be regulated with a state machine anddirected by operational inputs from memory and/or sensors; logiccomponent 520 may be organized according to Von Neumann and/or Harvardarchitecture as a non-limiting example. Logic component 520 may include,incorporate, and/or be incorporated in, without limitation, amicrocontroller, microprocessor, digital signal processor (DSP), FieldProgrammable Gate Array (FPGA), Complex Programmable Logic Device(CPLD), Graphical Processing Unit (GPU), general purpose GPU, TensorProcessing Unit (TPU), analog or mixed signal processor, TrustedPlatform Module (TPM), a floating point unit (FPU), and/or system on achip (SoC). In an embodiment, logic component 520 may include one ormore integrated circuit microprocessors, which may contain one or morecentral processing units, central processors, and/or main processors, ona single metal-oxide-semiconductor chip. Logic component 520 may beconfigured to execute a sequence of stored instructions to be performedon the output language and/or intermediate representation 512. Logiccomponent 520 may be configured to fetch and/or retrieve the instructionfrom a memory cache, wherein a “memory cache,” as used in thisdisclosure, is a stored instruction set on flight controller 504. Logiccomponent 520 may be configured to decode the instruction retrieved fromthe memory cache to opcodes and/or operands. Logic component 520 may beconfigured to execute the instruction on intermediate representation 512and/or output language. For example, and without limitation, logiccomponent 520 may be configured to execute an addition operation onintermediate representation 512 and/or output language.

In an embodiment, and without limitation, logic component 520 may beconfigured to calculate a flight element 524. As used in this disclosurea “flight element” is an element of datum denoting a relative status ofaircraft. For example, and without limitation, flight element 524 maydenote one or more torques, thrusts, airspeed velocities, forces,altitudes, groundspeed velocities, directions during flight, directionsfacing, forces, orientations, and the like thereof. For example, andwithout limitation, flight element 524 may denote that aircraft iscruising at an altitude and/or with a sufficient magnitude of forwardthrust. As a further non-limiting example, flight status may denote thatis building thrust and/or groundspeed velocity in preparation for atakeoff. As a further non-limiting example, flight element 524 maydenote that aircraft is following a flight path accurately and/orsufficiently.

Still referring to FIG. 5 , flight controller 504 may include a chipsetcomponent 528. As used in this disclosure a “chipset component” is acomponent that manages data flow. In an embodiment, and withoutlimitation, chipset component 528 may include a northbridge data flowpath, wherein the northbridge dataflow path may manage data flow fromlogic component 520 to a high-speed device and/or component, such as aRAM, graphics controller, and the like thereof. In another embodiment,and without limitation, chipset component 528 may include a southbridgedata flow path, wherein the southbridge dataflow path may manage dataflow from logic component 520 to lower-speed peripheral buses, such as aperipheral component interconnect (PCI), industry standard architecture(ICA), and the like thereof. In an embodiment, and without limitation,southbridge data flow path may include managing data flow betweenperipheral connections such as ethernet, USB, audio devices, and thelike thereof. Additionally or alternatively, chipset component 528 maymanage data flow between logic component 520, memory cache, and a flightcomponent 208. As used in this disclosure (and with particular referenceto FIG. 5 ) a “flight component” is a portion of an aircraft that can bemoved or adjusted to affect one or more flight elements. For example,flight component 208 may include a component used to affect theaircrafts' roll and pitch which may comprise one or more ailerons. As afurther example, flight component 208 may include a rudder to controlyaw of an aircraft. In an embodiment, chipset component 528 may beconfigured to communicate with a plurality of flight components as afunction of flight element 524. For example, and without limitation,chipset component 528 may transmit to an aircraft rotor to reduce torqueof a first lift propulsor and increase the forward thrust produced by apusher component to perform a flight maneuver.

In an embodiment, and still referring to FIG. 5 , flight controller 504may be configured generate an autonomous function. As used in thisdisclosure an “autonomous function” is a mode and/or function of flightcontroller 504 that controls aircraft automatically. For example, andwithout limitation, autonomous function may perform one or more aircraftmaneuvers, take offs, landings, altitude adjustments, flight levelingadjustments, turns, climbs, and/or descents. As a further non-limitingexample, autonomous function may adjust one or more airspeed velocities,thrusts, torques, and/or groundspeed velocities. As a furthernon-limiting example, autonomous function may perform one or more flightpath corrections and/or flight path modifications as a function offlight element 524. In an embodiment, autonomous function may includeone or more modes of autonomy such as, but not limited to, autonomousmode, semi-autonomous mode, and/or non-autonomous mode. As used in thisdisclosure “autonomous mode” is a mode that automatically adjusts and/orcontrols aircraft and/or the maneuvers of aircraft in its entirety. Forexample, autonomous mode may denote that flight controller 504 willadjust the aircraft. As used in this disclosure a “semi-autonomous mode”is a mode that automatically adjusts and/or controls a portion and/orsection of aircraft. For example, and without limitation,semi-autonomous mode may denote that a pilot will control thepropulsors, wherein flight controller 504 will control the aileronsand/or rudders. As used in this disclosure “non-autonomous mode” is amode that denotes a pilot will control aircraft and/or maneuvers ofaircraft in its entirety.

In an embodiment, and still referring to FIG. 5 , flight controller 504may generate autonomous function as a function of an autonomousmachine-learning model. As used in this disclosure an “autonomousmachine-learning model” is a machine-learning model to produce anautonomous function output given flight element 524 and a pilot signal536 as inputs; this is in contrast to a non-machine learning softwareprogram where the commands to be executed are determined in advance by auser and written in a programming language. As used in this disclosure a“pilot signal” is an element of datum representing one or more functionsa pilot is controlling and/or adjusting. For example, pilot signal 536may denote that a pilot is controlling and/or maneuvering ailerons,wherein the pilot is not in control of the rudders and/or propulsors. Inan embodiment, pilot signal 536 may include an implicit signal and/or anexplicit signal. For example, and without limitation, pilot signal 536may include an explicit signal, wherein the pilot explicitly statesthere is a lack of control and/or desire for autonomous function. As afurther non-limiting example, pilot signal 536 may include an explicitsignal directing flight controller 504 to control and/or maintain aportion of aircraft, a portion of the flight plan, the entire aircraft,and/or the entire flight plan. As a further non-limiting example, pilotsignal 536 may include an implicit signal, wherein flight controller 504detects a lack of control such as by a malfunction, torque alteration,flight path deviation, and the like thereof. In an embodiment, andwithout limitation, pilot signal 536 may include one or more explicitsignals to reduce torque, and/or one or more implicit signals thattorque may be reduced due to reduction of airspeed velocity. In anembodiment, and without limitation, pilot signal 536 may include one ormore local and/or global signals. For example, and without limitation,pilot signal 536 may include a local signal that is transmitted by apilot and/or crew member. As a further non-limiting example, pilotsignal 536 may include a global signal that is transmitted by airtraffic control and/or one or more remote users that are incommunication with the pilot of aircraft. In an embodiment, pilot signal536 may be received as a function of a tri-state bus and/or multiplexorthat denotes an explicit pilot signal should be transmitted prior to anyimplicit or global pilot signal.

Still referring to FIG. 5 , autonomous machine-learning model mayinclude one or more autonomous machine-learning processes such assupervised, unsupervised, or reinforcement machine-learning processesthat flight controller 504 and/or a remote device may or may not use inthe generation of autonomous function. As used in this disclosure“remote device” is an external device to flight controller 504.Additionally or alternatively, autonomous machine-learning model mayinclude one or more autonomous machine-learning processes that afield-programmable gate array (FPGA) may or may not use in thegeneration of autonomous function. Autonomous machine-learning processmay include, without limitation machine learning processes such assimple linear regression, multiple linear regression, polynomialregression, support vector regression, ridge regression, lassoregression, elasticnet regression, decision tree regression, randomforest regression, logistic regression, logistic classification,K-nearest neighbors, support vector machines, kernel support vectormachines, naïve bayes, decision tree classification, random forestclassification, K-means clustering, hierarchical clustering,dimensionality reduction, principal component analysis, lineardiscriminant analysis, kernel principal component analysis, Q-learning,State Action Reward State Action (SARSA), Deep-Q network, Markovdecision processes, Deep Deterministic Policy Gradient (DDPG), or thelike thereof.

In an embodiment, and still referring to FIG. 5 , autonomous machinelearning model may be trained as a function of autonomous training data,wherein autonomous training data may correlate a flight element, pilotsignal, and/or simulation data to an autonomous function. For example,and without limitation, a flight element of an airspeed velocity, apilot signal of limited and/or no control of propulsors, and asimulation data of required airspeed velocity to reach the destinationmay result in an autonomous function that includes a semi-autonomousmode to increase thrust of the propulsors. Autonomous training data maybe received as a function of user-entered valuations of flight elements,pilot signals, simulation data, and/or autonomous functions. Flightcontroller 504 may receive autonomous training data by receivingcorrelations of flight element, pilot signal, and/or simulation data toan autonomous function that were previously received and/or determinedduring a previous iteration of generation of autonomous function.Autonomous training data may be received by one or more remote devicesand/or FPGAs that at least correlate a flight element, pilot signal,and/or simulation data to an autonomous function. Autonomous trainingdata may be received in the form of one or more user-enteredcorrelations of a flight element, pilot signal, and/or simulation datato an autonomous function.

Still referring to FIG. 5 , flight controller 504 may receive autonomousmachine-learning model from a remote device and/or FPGA that utilizesone or more autonomous machine learning processes, wherein a remotedevice and an FPGA is described above in detail. For example, andwithout limitation, a remote device may include a computing device,external device, processor, FPGA, microprocessor and the like thereof.Remote device and/or FPGA may perform the autonomous machine-learningprocess using autonomous training data to generate autonomous functionand transmit the output to flight controller 504. Remote device and/orFPGA may transmit a signal, bit, datum, or parameter to flightcontroller 504 that at least relates to autonomous function.Additionally or alternatively, the remote device and/or FPGA may providean updated machine-learning model. For example, and without limitation,an updated machine-learning model may be comprised of a firmware update,a software update, an autonomous machine-learning process correction,and the like thereof. As a non-limiting example, a software update mayincorporate a new simulation data that relates to a modified flightelement. Additionally or alternatively, the updated machine learningmodel may be transmitted to the remote device and/or FPGA, wherein theremote device and/or FPGA may replace the autonomous machine-learningmodel with the updated machine-learning model and generate theautonomous function as a function of the flight element, pilot signal,and/or simulation data using the updated machine-learning model. Theupdated machine-learning model may be transmitted by the remote deviceand/or FPGA and received by flight controller 504 as a software update,firmware update, or corrected autonomous machine-learning model. Forexample, and without limitation autonomous machine learning model mayutilize a neural net machine-learning process, wherein the updatedmachine-learning model may incorporate a gradient boostingmachine-learning process.

Still referring to FIG. 5 , flight controller 504 may include, beincluded in, and/or communicate with a mobile device such as a mobiletelephone or smartphone. Further, flight controller may communicate withone or more additional devices as described below in further detail viaa network interface device. The network interface device may be utilizedfor commutatively connecting a flight controller to one or more of avariety of networks, and one or more devices. Examples of a networkinterface device include, but are not limited to, a network interfacecard (e.g., a mobile network interface card, a LAN card), a modem, andany combination thereof. Examples of a network include, but are notlimited to, a wide area network (e.g., the Internet, an enterprisenetwork), a local area network (e.g., a network associated with anoffice, a building, a campus or other relatively small geographicspace), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computingdevices, and any combinations thereof. The network may include anynetwork topology and can may employ a wired and/or a wireless mode ofcommunication.

In an embodiment, and still referring to FIG. 5 , flight controller 504may include, but is not limited to, for example, a cluster of flightcontrollers in a first location and a second flight controller orcluster of flight controllers in a second location. Flight controller504 may include one or more flight controllers dedicated to datastorage, security, distribution of traffic for load balancing, and thelike. Flight controller 504 may be configured to distribute one or morecomputing tasks as described below across a plurality of flightcontrollers, which may operate in parallel, in series, redundantly, orin any other manner used for distribution of tasks or memory betweencomputing devices. For example, and without limitation, flightcontroller 504 may implement a control algorithm to distribute and/orcommand the plurality of flight controllers. As used in this disclosurea “control algorithm” is a finite sequence of well-defined computerimplementable instructions that may determine the flight component ofthe plurality of flight components to be adjusted. For example, andwithout limitation, control algorithm may include one or more algorithmsthat reduce and/or prevent aviation asymmetry. As a further non-limitingexample, control algorithms may include one or more models generated asa function of a software including, but not limited to Simulink byMathWorks, Natick, Massachusetts, USA. In an embodiment, and withoutlimitation, control algorithm may be configured to generate anauto-code, wherein an “auto-code,” is used herein, is a code and/oralgorithm that is generated as a function of the one or more modelsand/or software's. In another embodiment, control algorithm may beconfigured to produce a segmented control algorithm. As used in thisdisclosure a “segmented control algorithm” is control algorithm that hasbeen separated and/or parsed into discrete sections. For example, andwithout limitation, segmented control algorithm may parse controlalgorithm into two or more segments, wherein each segment of controlalgorithm may be performed by one or more flight controllers operatingon distinct flight components.

In an embodiment, and still referring to FIG. 5 , control algorithm maybe configured to determine a segmentation boundary as a function ofsegmented control algorithm. As used in this disclosure a “segmentationboundary” is a limit and/or delineation associated with the segments ofthe segmented control algorithm. For example, and without limitation,segmentation boundary may denote that a segment in the control algorithmhas a first starting section and/or a first ending section. As a furthernon-limiting example, segmentation boundary may include one or moreboundaries associated with an ability of flight component 208. In anembodiment, control algorithm may be configured to create an optimizedsignal communication as a function of segmentation boundary. Forexample, and without limitation, optimized signal communication mayinclude identifying the discrete timing required to transmit and/orreceive the one or more segmentation boundaries. In an embodiment, andwithout limitation, creating optimized signal communication furthercomprises separating a plurality of signal codes across the plurality offlight controllers. For example, and without limitation the plurality offlight controllers may include one or more formal networks, whereinformal networks transmit data along an authority chain and/or arelimited to task-related communications. As a further non-limitingexample, communication network may include informal networks, whereininformal networks transmit data in any direction. In an embodiment, andwithout limitation, the plurality of flight controllers may include achain path, wherein a “chain path,” as used herein, is a linearcommunication path comprising a hierarchy that data may flow through. Inan embodiment, and without limitation, the plurality of flightcontrollers may include an all-channel path, wherein an “all-channelpath,” as used herein, is a communication path that is not restricted toa particular direction. For example, and without limitation, data may betransmitted upward, downward, laterally, and the like thereof. In anembodiment, and without limitation, the plurality of flight controllersmay include one or more neural networks that assign a weighted value toa transmitted datum. For example, and without limitation, a weightedvalue may be assigned as a function of one or more signals denoting thata flight component is malfunctioning and/or in a failure state.

Still referring to FIG. 5 , the plurality of flight controllers mayinclude a master bus controller. As used in this disclosure a “masterbus controller” is one or more devices and/or components that areconnected to a bus to initiate a direct memory access transaction,wherein a bus is one or more terminals in a bus architecture. Master buscontroller may communicate using synchronous and/or asynchronous buscontrol protocols. In an embodiment, master bus controller may includeflight controller 504. In another embodiment, master bus controller mayinclude one or more universal asynchronous receiver-transmitters (UART).For example, and without limitation, master bus controller may includeone or more bus architectures that allow a bus to initiate a directmemory access transaction from one or more buses in the busarchitectures. As a further non-limiting example, master bus controllermay include one or more peripheral devices and/or components tocommunicate with another peripheral device and/or component and/or themaster bus controller. In an embodiment, master bus controller may beconfigured to perform bus arbitration. As used in this disclosure “busarbitration” is method and/or scheme to prevent multiple buses fromattempting to communicate with and/or connect to master bus controller.For example and without limitation, bus arbitration may include one ormore schemes such as a small computer interface system, wherein a smallcomputer interface system is a set of standards for physical connectingand transferring data between peripheral devices and master buscontroller by defining commands, protocols, electrical, optical, and/orlogical interfaces. In an embodiment, master bus controller may receiveintermediate representation 512 and/or output language from logiccomponent 520, wherein output language may include one or moreanalog-to-digital conversions, low bit rate transmissions, messageencryptions, digital signals, binary signals, logic signals, analogsignals, and the like thereof described above in detail.

Still referring to FIG. 5 , master bus controller may communicate with aslave bus. As used in this disclosure a “slave bus” is one or moreperipheral devices and/or components that initiate a bus transfer. Forexample, and without limitation, slave bus may receive one or morecontrols and/or asymmetric communications from master bus controller,wherein slave bus transfers data stored to master bus controller. In anembodiment, and without limitation, slave bus may include one or moreinternal buses, such as but not limited to a/an internal data bus,memory bus, system bus, front-side bus, and the like thereof. In anotherembodiment, and without limitation, slave bus may include one or moreexternal buses such as external flight controllers, external computers,remote devices, printers, aircraft computer systems, flight controlsystems, and the like thereof.

In an embodiment, and still referring to FIG. 5 , control algorithm mayoptimize signal communication as a function of determining one or morediscrete timings. For example, and without limitation master buscontroller may synchronize timing of the segmented control algorithm byinjecting high priority timing signals on a bus of the master buscontrol. As used in this disclosure a “high priority timing signal” isinformation denoting that the information is important. For example, andwithout limitation, high priority timing signal may denote that asection of control algorithm is of high priority and should be analyzedand/or transmitted prior to any other sections being analyzed and/ortransmitted. In an embodiment, high priority timing signal may includeone or more priority packets. As used in this disclosure a “prioritypacket” is a formatted unit of data that is communicated between theplurality of flight controllers. For example, and without limitation,priority packet may denote that a section of control algorithm should beused and/or is of greater priority than other sections.

Still referring to FIG. 5 , flight controller 504 may also beimplemented using a “shared nothing” architecture in which data iscached at the worker, in an embodiment, this may enable scalability ofaircraft and/or computing device. Flight controller 504 may include adistributer flight controller. As used in this disclosure a “distributerflight controller” is a component that adjusts and/or controls aplurality of flight components as a function of a plurality of flightcontrollers. For example, distributer flight controller may include aflight controller that communicates with a plurality of additionalflight controllers and/or clusters of flight controllers. In anembodiment, distributed flight control may include one or more neuralnetworks. For example, neural network also known as an artificial neuralnetwork, is a network of “nodes,” or data structures having one or moreinputs, one or more outputs, and a function determining outputs based oninputs. Such nodes may be organized in a network, such as withoutlimitation a convolutional neural network, including an input layer ofnodes, one or more intermediate layers, and an output layer of nodes.Connections between nodes may be created via the process of “training”the network, in which elements from a training dataset are applied tothe input nodes, a suitable training algorithm (such asLevenberg-Marquardt, conjugate gradient, simulated annealing, or otheralgorithms) is then used to adjust the connections and weights betweennodes in adjacent layers of the neural network to produce the desiredvalues at the output nodes. This process is sometimes referred to asdeep learning.

Still referring to FIG. 5 , a node may include, without limitation aplurality of inputs x; that may receive numerical values from inputs toa neural network containing the node and/or from other nodes. Node mayperform a weighted sum of inputs using weights w, that are multiplied byrespective inputs xi. Additionally or alternatively, a bias b may beadded to the weighted sum of the inputs such that an offset is added toeach unit in the neural network layer that is independent of the inputto the layer. The weighted sum may then be input into a function p,which may generate one or more outputs y. Weight w, applied to an inputx; may indicate whether the input is “excitatory,” indicating that ithas strong influence on the one or more outputs y, for instance by thecorresponding weight having a large numerical value, and/or a“inhibitory,” indicating it has a weak effect influence on the one moreinputs y, for instance by the corresponding weight having a smallnumerical value. The values of weights w, may be determined by traininga neural network using training data, which may be performed using anysuitable process as described above. In an embodiment, and withoutlimitation, a neural network may receive semantic units as inputs andoutput vectors representing such semantic units according to weights w,that are derived using machine-learning processes as described in thisdisclosure.

Still referring to FIG. 5 , flight controller may include asub-controller 540. As used in this disclosure a “sub-controller” is acontroller and/or component that is part of a distributed controller asdescribed above; for instance, flight controller 504 may be and/orinclude a distributed flight controller made up of one or moresub-controllers. For example, and without limitation, sub-controller 540may include any controllers and/or components thereof that are similarto distributed flight controller and/or flight controller as describedabove. Sub-controller 540 may include any component of any flightcontroller as described above. Sub-controller 540 may be implemented inany manner suitable for implementation of a flight controller asdescribed above. As a further non-limiting example, sub-controller 540may include one or more processors, logic components and/or computingdevices capable of receiving, processing, and/or transmitting dataacross the distributed flight controller as described above. As afurther non-limiting example, sub-controller 540 may include acontroller that receives a signal from a first flight controller and/orfirst distributed flight controller component and transmits the signalto a plurality of additional sub-controllers and/or flight components.

Still referring to FIG. 5 , flight controller may include aco-controller 544. As used in this disclosure a “co-controller” is acontroller and/or component that joins flight controller 504 ascomponents and/or nodes of a distributer flight controller as describedabove. For example, and without limitation, co-controller 544 mayinclude one or more controllers and/or components that are similar toflight controller 504. As a further non-limiting example, co-controller544 may include any controller and/or component that joins flightcontroller 504 to distributer flight controller. As a furthernon-limiting example, co-controller 544 may include one or moreprocessors, logic components and/or computing devices capable ofreceiving, processing, and/or transmitting data to and/or from flightcontroller 504 to distributed flight control system. Co-controller 544may include any component of any flight controller as described above.Co-controller 544 may be implemented in any manner suitable forimplementation of a flight controller as described above.

In an embodiment, and with continued reference to FIG. 5 , flightcontroller 504 may be designed and/or configured to perform any method,method step, or sequence of method steps in any embodiment described inthis disclosure, in any order and with any degree of repetition. Forinstance, flight controller 504 may be configured to perform a singlestep or sequence repeatedly until a desired or commanded outcome isachieved; repetition of a step or a sequence of steps may be performediteratively and/or recursively using outputs of previous repetitions asinputs to subsequent repetitions, aggregating inputs and/or outputs ofrepetitions to produce an aggregate result, reduction or decrement ofone or more variables such as global variables, and/or division of alarger processing task into a set of iteratively addressed smallerprocessing tasks. Flight controller may perform any step or sequence ofsteps as described in this disclosure in parallel, such assimultaneously and/or substantially simultaneously performing a step twoor more times using two or more parallel threads, processor cores, orthe like; division of tasks between parallel threads and/or processesmay be performed according to any protocol suitable for division oftasks between iterations. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in whichsteps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

FIG. 6 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 600 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 600 includes a processor 604 and a memory608 that communicate with each other, and with other components, via abus 612. Bus 612 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 604 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 604 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 604 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating pointunit (FPU), and/or system on a chip (SoC).

Memory 608 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 616 (BIOS), including basic routines that help totransfer information between elements within computer system 600, suchas during start-up, may be stored in memory 608. Memory 608 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 620 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 608 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 600 may also include a storage device 624. Examples of astorage device (e.g., storage device 624) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 624 may be connected to bus 612 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 624 (or one or morecomponents thereof) may be removably interfaced with computer system 600(e.g., via an external port connector (not shown)). Particularly,storage device 624 and an associated machine-readable medium 628 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 600. In one example, software 620 may reside, completelyor partially, within machine-readable medium 628. In another example,software 620 may reside, completely or partially, within processor 604.

Computer system 600 may also include an input device 632. In oneexample, a user of computer system 600 may enter commands and/or otherinformation into computer system 600 via input device 632. Examples ofan input device 632 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 632may be interfaced to bus 612 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 612, and any combinations thereof. Input device 632 mayinclude a touch screen interface that may be a part of or separate fromdisplay 636, discussed further below. Input device 632 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 600 via storage device 624 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 640. A network interfacedevice, such as network interface device 640, may be utilized forconnecting computer system 600 to one or more of a variety of networks,such as network 644, and one or more remote devices 648 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 644,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 620,etc.) may be communicated to and/or from computer system 600 via networkinterface device 640.

Computer system 600 may further include a video display adapter 652 forcommunicating a displayable image to a display device, such as displaydevice 636. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 652 and display device 636 may be utilized incombination with processor 604 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 600 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 612 via a peripheral interface 656. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

1. A propulsor gauge of an electric aircraft, the gauge comprising: aprocessor; a memory communicatively connected to the processor andconfigured to contain instructions configuring the processor to: receivecondition data of an electric aircraft, wherein the condition datacomprises an angle of attack of an electric aircraft, identify a thrustenvelope parameter of a propulsor of the electric aircraft; determine arecommended range as a function of the thrust envelope parameter and thecondition data, wherein the recommended range comprises a lowerthreshold and an upper threshold; and store the lower threshold and theupper threshold in a lookup table: an indicator configured to indicatethe thrust envelope parameter; and a display communicatively connectedto the processor and configured to display a visual representation ofthe recommended range.
 2. The gauge of claim 1, wherein the visualrepresentation comprises a band, wherein the band comprises an upperbound and a lower bound indicating the upper threshold and the lowerthreshold, respectively.
 3. The gauge of claim 1, wherein the processoris further configured to: identify an updated thrust envelope parameter;and determine an updated recommended range as a function of the updatedpropulsor parameter.
 4. (canceled)
 5. The gauge of claim 1, furthercomprising a sensor, wherein the sensor detects the thrust envelopeparameter or the condition data of the electric aircraft.
 6. The gaugeof claim 5, wherein the sensor comprises an encoder.
 7. (canceled) 8.The gauge of claim 1, wherein the visual representation comprises aneedle.
 9. The gauge of claim 1, wherein determining the recommendedrange comprises: receiving a training data set correlating currentcondition inputs with recommended RPM outputs; and generating amachine-learning model as a function of the training data set.
 10. Thegauge of claim 1, wherein the display includes an LCD display.
 11. Thegauge of claim 1, wherein the thrust envelope parameter comprises arevolution per minute (RPM) of the propulsor.
 12. The gauge of claim 1,wherein the thrust envelope parameter comprises a speed of thepropulsor.
 13. The gauge of claim 1, wherein the thrust envelopeparameter comprises a pilot input control.
 14. The gauge of claim 1,wherein the propulsor comprises a propeller.
 15. A method of operatingan electric aircraft using a propulsor gauge, the method comprising:receiving, by a processor, condition data of an electric aircraft,wherein the condition data comprises an angle of attack of the electricaircraft; identifying, by the processor, a thrust envelope parameter ofa propulsor of the electric aircraft; determining, by the processor, arecommended range as a function of the thrust envelope parameter and thecondition data, wherein the recommended range comprises a lowerthreshold and an upper threshold; storing the lower threshold and theupper threshold in a lookup table; indicating, by an indicator, thethrust envelope parameter; and displaying, by a display communicativelyconnected to the processor, a visual representation of the recommendedrange.
 16. The method of claim 5, wherein the visual representationcomprises a band, wherein the band comprises an upper bound and a lowerbound indicating the upper threshold and the lower threshold,respectively.
 17. The gauge method of claim 5, wherein the processor isfurther configured to: identify an updated thrust envelope parameter;and determine an updated recommended range as a function of the updatedthrust envelope parameter.
 18. (canceled)
 19. The gauge of claim 1,wherein: the visual representation of the recommended range comprises alower bound and an upper bound, wherein the lower bound and the upperbound are stored in the lookup table; and the indicator is directedtoward a region between the lower bound and the upper bound duringnormal operation of the electric aircraft.
 20. The gauge of claim 1,wherein the processor is further configured to determine one or moregradations of the recommended range, wherein the one or more gradationscomprise a first gradation comprising an optimal zone and a secondgradation comprising a buffer zone.
 21. The gauge of claim 1, furthercomprising an alert component communicatively connected to theprocessor, wherein the alert component is configured to activate if aposition of the indicator is outside of the recommended range.
 22. Themethod of claim 15, wherein: the visual representation of therecommended range comprises a lower bound and an upper bound, whereinthe lower bound and the upper bound are stored in the lookup table; andthe indicator is directed toward a region between the lower bound andthe upper bound during normal operation of the electric aircraft. 23.The method of claim 15, further comprising activating an alert componentif a position of the indicator is outside of the recommended range.